On the Generalization of Machine-Learning-aided QoT Estimation in Optical Networks

This paper presents a composable machine learning method for generalizing the quality-of-transmission (QoT) metric estimation in optical networks. The composable machine learning approach characterizes this metric for lightpaths of arbitrary lengths by compositions of launch, propagation and readout modules. Results verify the feasibility of the design and show its successful application in facilitating autonomous lightpath provisioning.